Online and offline handwritten Chinese text recognition (HTCR) has been
studied for decades. Early methods adopted oversegmentation-based strategies
but suffered from low speed, insufficient accuracy, and high cost of character
segmentation annotations. Recently, segmentation-free methods based on
connectionist temporal classification (CTC) and attention mechanism, have
dominated the field of HCTR. However, people actually read text character by
character, especially for ideograms such as Chinese. This raises the question:
are segmentation-free strategies really the best solution to HCTR? To explore
this issue, we propose a new segmentation-based method for recognizing
handwritten Chinese text that is implemented using a simple yet efficient fully
convolutional network. A novel weakly supervised learning method is proposed to
enable the network to be trained using only transcript annotations; thus, the
expensive character segmentation annotations required by previous
segmentation-based methods can be avoided. Owing to the lack of context
modeling in fully convolutional networks, we propose a contextual
regularization method to integrate contextual information into the network
during the training stage, which can further improve the recognition
performance. Extensive experiments conducted on four widely used benchmarks,
namely CASIA-HWDB, CASIA-OLHWDB, ICDAR2013, and SCUT-HCCDoc, show that our
method significantly surpasses existing methods on both online and offline
HCTR, and exhibits a considerably higher inference speed than
CTC/attention-based approaches.

提出一种基于分割的方法以识别手写中文文本，使用简单而有效的完全卷积网络实现。使用一种新颖的弱监督学习方法，只需使用交录注释即可训练网络；无需先前分割方法所需的昂贵字符分割标注。因为完全卷积网络缺乏上下文建模，所以提出了一种上下文规则化方法将上下文信息纳入训练，进一步提高了识别性能。在四个广泛使用的基准测试上进行了广泛实验，即 CASIA-HWDB，CASIA-OLHWDB，ICDAR2013 和 SCUT-HCCDoc，结果表明我们的方法在在线和离线 HCTR 上都显著优于现有方法，并且比 CTC / 基于注意力的方法具有更高的推断速度。